Hi all,

     I'm picking up on an older thread (https://stat.ethz.ch/pipermail/bioconductor/2006-August/013881.html 
) which poses the following question: What are the most appropriate  
methods for normalization in the face of large numbers of  
differentially expressed genes?  Aside from this 2006 thread, I've  
found only one publication on the topic (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19040742 
) but the authors, unfortunately for me, have built their software  
using Matlab.

My data consists of Agilent arrays on eight (very different) tissues  
hybridized against a common reference sample.  In six of the  
hybridization, Cy3 is the reference and Cy5 is the tissue in  
question.  In two of the arrays, the situation is reversed.

I thought I would throw open the doors again with the following  
questions:

1) In the three years since aforementioned post, are there better  
methods that I could look into for normalizing in the face of large  
gene effects?

2) How "robust" are loess-based normalization methods?  I've been  
using the limma package for within array normalization using global  
loess (no print tips on Agilent arrays), but its frankly hard to  
evaluate the consequences given the relatively few numbers of spike-ins.

3) Ye olde background correction issue:  Because the background does  
not appear all that uniform on my slides, I would like to do a local  
background correction.  But what to do about those damn negative  
numbers? My current thoughts is to carry out a background subtraction  
using "min" in limma so that negative values are set to be equal to  
the smallest positive value on the array.  But perhaps it is better to  
set the small values = 1 to avoid issues in the log transformation?   
I'm also concerned about an effect of "min" subtraction that can be  
seen in the following density plots:

Before subtraction: http://www.duke.edu/~dag23/BioCQs/densityPlot_RG_originalData.pdf
After subtraction: http://www.duke.edu/~dag23/BioCQs/densityPlot_RG_minSubtracted.pdf

These later artifacts appear to lead to an unfortunate pattern after  
loess normalization (http://www.duke.edu/~dag23/BioCQs/densityPlot_MA_minSubtracted.pdf 
)

Its a great many questions, I know, but any insight would be greatly  
appreciated.  I suspect that these issues comes up more frequently  
than I realize, so I apologize if I've missed an earlier thread.


Best wishes,

David



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